This invention relates to artificial neurons, that is, devices which simulate the function of biological nerve cells. More particularly, this invention describes electronic circuitry arranged to reproduce typical functions of different types of neurons. The simulation translates the functional structure of biological nerve cells from modern neuroscience into electronics.
There has been considerable prior activity in attempting to develop simulations for the actions of neurons as they interrelate in nervous systems or "networks". Individual neurons can be represented schematically as shown in FIGS. 1 and 2. Networks are made up of great numbers of interconnected neurons with the points of connection being referred to as synapses. Each synapse involves the connection of one neuron sending a signal and one neuron receiving the signal. The neuron cell body is referred to as the soma. This serves to integrate all stimulation received from its synapses and respond with an output signal only if a certain threshold is exceeded. Prior attempts to replicate the functions of individual neurons have been rudimentary and have produced results quite remote from realistic simulation of actual nerve cells.
There has been substantial prior work devoted to simulating neuron networks. One development in that field has been the so-called "modifiable synapse". In such a device the degree of stimulation sensed by the receiving neuron at a given time due to input activity at the synapse has been adjusted according to previous activity or some interpretation thereof. Modifying the sensitivities of such synapses is intended to supply memory and adaptiveness in these systems. The neurons are "trained" within the network either manually or automatically, based on the total system's response to stimulation, in order to achieve the desired system responses as consistently as possible. In this way, networks have been able to achieve some degree of pattern recognition with respect to groups of input signals applied to the system. Specific tuning of each neuron's memory based on desired total system response, however, tends to leave the system capable of recognizing only a small group of input patterns. Such intentional "training" of the neurons does not occur in biological systems, and thus is basically not appropriate in a simulated system.
Most prior work on neuron simulation includes some degree of stimulus integration, however, the arrangements are simple and do not allow for a multitude of neuron types which are known to occur in biological systems. Other aspects of nerve cell function are similarly neglected in the prior work. Recreation of the valuable functions of neurons requires an accurate electronic simulation which has not been accomplished in previous designs.